Staff Network Engineer (L6/L7) - AI & ML Infrastructure
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We’re seeking a Staff Network Engineer (L6/L7) focused on designing, scaling, and optimizing large-scale RoCEv2 data center networks powering next-generation AI and ML infrastructure.
You’ll work closely with network architects and infrastructure leadership to execute network designs and ensure operational stability as we scale cluster sizes to over 100,000 GPUs. You will drive multi-team technical direction, influence cross-functional outcomes, and own critical components of network performance and reliability. You’ll remain hands-on with high-speed optics, switching, routing, and congestion management in production clusters, while developing the standards and automation tooling that other engineers build and operate against.
What You’ll Do
Set multi-team technical direction and influence cross-functional network architecture decisions to ensure seamless infrastructure scaling.
Design, deploy, and optimize large-scale RoCEv2 data center networks supporting AI and ML clusters from thousands to 100,000+ GPUs.
Implement and tune congestion management protocols across RDMA fabrics, including PFC, ECN, and DCQCN, based on real production telemetry.
Build and standardize automation, validation, and observability patterns that prevent misconfiguration and reduce manual operational overhead.
Serve as a senior technical escalation point for complex network failures and actively mentor mid-level engineers in advanced troubleshooting and design.
Who You Are
Required Qualifications
A Bachelor’s degree in Computer Science, Electrical Engineering, or a related technical field, or equivalent practical experience.
Extensive experience deploying and operating RDMA and RoCEv2 networks in large-scale production environments supporting AI or HPC workloads.
Advanced knowledge of switching hardware and NOS, such as Arista, Juniper, and custom solutions using SONiC, including high-speed Ethernet fabrics.
Proven hands-on experience with congestion management and performance tuning using PFC, ECN, and DCQCN.
Strong experience with high-speed optics and cabling including 400G, 800G, AEC, AOC, DAC, and structured cabling at scale.
Strong automation mindset, with experience using Python, Ansible, Terraform, Git, and production observability tooling.
Demonstrated ability to autonomously solve complex, novel problems and translate broad architectural goals into executable project plans.
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TensorWave
View Company ProfileTensorWave is a premier, enterprise-grade cloud infrastructure provider engineered to orchestrate massive-scale artificial intelligence (AI) and high-performance computing (HPC) workloads. Operating as a high-velocity digital ecosystem, the company eliminates the operational friction and supply chain bottlenecks of legacy hyperscalers by exclusively deploying advanced AMD Instinct™ accelerators—including the MI300X and MI325X—across highly optimized bare-metal environments. Moving beyond the rigid memory constraints of traditional GPU cloud models, TensorWave provides industry-leading capacity with up to 288GB of HBM3e per accelerator, directly tackling the immense requirements of next-generation Large Language Models (LLMs) and complex machine learning training clusters. Under the hood, their UEC-ready networking architecture and direct liquid cooling systems seamlessly integrate into existing AI pipelines, ensuring ultra-low latency inference and blistering training speeds. What sets TensorWave apart is its uncompromising dedication to open-source democratization and performance accessibility; by bridging the gap between top-tier compute resources and massive cost-efficiency, the platform empowers scaling enterprises to radically accelerate AI innovation without the burden of building internal hardware infrastructure.
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